Analysis of Variance
Department of Educational Psychology
Agenda
1 Overview and Introduction
2 Solutions for Assumption Violations
3 Conclusion
Agenda
1 Overview and Introduction
2 Solutions for Assumption Violations
3 Conclusion
Without going into too much detail, we are concerned with how robust a test is, or how resilient a test is to assumption violations, and how well it works under less-than-ideal circumstances
Some researcher’s hold that many commonly used tests, i.e., t-tests are reasonably robust at baseline to assumption violations
Making mathematical variable transformations is largely used to address the [Normality Assumption], but maybe indirectly solve other issues as well
The exact transformation is dependent on the type of problem, specifically the skew:
Advantages:
Disadvantages:
Another option, particularly useful for negatively kurtotic (platykurtic) distributions (relatively flat distributions with an unusual number of observations in the tails) is to use variable trimming.
A trimmed sample is a sample where a fixed percentage of extreme values is removed from each tail.
Another related option is using winsorizing
Advantages:
Disadvantages:
Agenda
1 Overview and Introduction
2 Solutions for Assumption Violations
3 Conclusion
Module 3 Lecture - Transformations and Non-parametric Comparisons for Two Groups || Analysis of Variance